When
July 11-14
Moscow time: 10:00-13:00, Beijing time: 15:00-18:00
About
The first China-Russia Conference on Numerical Optimization and Machine Learning (NOML 2022) will be hold online, from July 11 to July 14, 2022. This conference is organized by the School of Mathematical Science of Beihang University, Beihang International Center for Mathematical Research, Laboratory of Computational Intelligence of Skoltech, and Center for Artificial Intelligence Technology of Skoltech.
The main aim of this conference is to bring together Russian and Chinese researchers actively working on numerical optimization and machine learning to exchange ideas, views, and new results and to build up contacts for future cooperation. All areas of numerical optimization and machine learning are of interest at NOML 2022. These include, but are not limited to:
- Algorithms and theory in nonlinear optimization
- Numerical methods for tensor computing
- Machine learning
- Mathematical foundations in artificial intelligence
Schedule
Monday (July, 11)
Moscow time
Beijing time
Speaker
15:10-16:00
Dual Quaternions and Their Applications
Liqun Qi
(The Hongkong Polytechnic University)
17:30-18:00
Iteratively reweighted methods for regularization: properties, complexity and acceleration
Hao Wang
(ShanghaiTech University)
Tuesday (July, 12)
Moscow time
Beijing time
Speaker
15:00-15:40
Tutorial: How to make convex optimization problems differentiable and combine them with neural networks?
Alexandr Katrutsa
(Skoltech)
16:30-17:00
Understanding the convergence of the preconditioned PDHG method: a view of indefinite proximal ADMM
Yumin Ma
(Nanjing Normal University)
Wednesday (July, 13)
Moscow time
Beijing time
Speaker
15:00-15:40
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
Konstantin Sozykin
(Skoltech)
15:40-16:10
Randomized Douglas-Rachford algorithm for linear systems: Improved accuracy and efficiency
Jiaxin Xie
(Beihang University)
16:30-17:00
Inexact first-order primal-dual methods for a class of saddle point problems
Zhongming Wu
(Nanjing University of Information Science and Technology)
17:00-17:30
Sketching and alternating projections for low-rank nonnegative matrix and tensor decompositions
Sergey Matveev
(INM RAS, Moscow)
17:30-18:00
Accelerated doubly stochastic gradient method for tensor CP decomposition
Chunfeng Cui
(Beihang University)
Tuesday (July, 14)
Moscow time
Beijing time
Speaker
15:30-16:00
Inertial alternating structure-adapted proximal (-like) gradient method
for nonconvex nonsmooth optimization problems
Xue Gao
(Hebei University of Technology)
16:20-16:50
A Double Extrapolation Primal-Dual Algorithm for Saddle Point Problems
Kai Wang
(Nanjing University of Science and Technology)
16:50-17:20
Task-based parallel programming model for neural networks
Aleksandr Mikhalev
(Skoltech)
17:20-17:50
Toward non-quadratic -lemma and its extension: new theory and application in nonconvex optimization
Meijia Yang
(University of Science and Technology Beijing)
